Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 24B | 30B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 256k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.05Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.20Novita |
| — | $20.00Novita · fp4 | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 28% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 68% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 55.7% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 68.3% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 22.5% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 73% |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Dec 9 2025 | Dec 15 2025 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Devstral Small 2 (24B)vsClaude Haiku 5.5Nemotron 3 NanovsClaude Haiku 5.5Devstral Small 2 (24B)vsGPT-6.1 SolNemotron 3 NanovsGPT-6.1 SolDevstral Small 2 (24B)vsGemini 4 ArgonNemotron 3 NanovsGemini 4 ArgonDevstral Small 2 (24B)vsMuse Spark OpenNemotron 3 NanovsMuse Spark OpenDevstral Small 2 (24B)vsGrok 4.7Nemotron 3 NanovsGrok 4.7Devstral Small 2 (24B)vsDeepSeek-V4.1-Flash-1017Nemotron 3 NanovsDeepSeek-V4.1-Flash-1017Frequently asked questions
Devstral Small 2 (24B) and Nemotron 3 Nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Devstral Small 2 (24B) shipped 6 days before Nemotron 3 Nano, so benchmark comparisons should account for the intervening progress.
Devstral Small 2 (24B) has 24B parameters, while Nemotron 3 Nano has 30B. Context windows are 256k (Devstral Small 2 (24B)) vs 1M (Nemotron 3 Nano). Devstral Small 2 (24B) is open weight, while Nemotron 3 Nano is open source.
Direct benchmark comparisons are unavailable — Devstral Small 2 (24B) and Nemotron 3 Nano don't publish scores on any of the same benchmarks.